Cellular senescence is a state in which cells become arrested in the cell cycle. That is, permanently stop dividing in response to stress or damage. The term “senescence” comes from the Latin senescere, meaning “to grow old.

The GenAge Database contains genes potentially associated with human aging and longevity.

Key Statistics

Statistical hypothesis testing is a framework for making decisions about populations based on sample data.

• Null Hypothesis (\(H_0\)): A statement of no effect or no difference.
• Alternative Hypothesis (\(H_1\) or \(H_A\)): A statement that contradicts the null hypothesis.

Mathematical Framework

The test statistic \(t\) for comparing two means is as follows: \[t = \frac{\bar{X}_1 - \bar{X}_2}{\sqrt{\frac{s_1^2}{n_1} + \frac{s_2^2}{n_2}}}\]

Where \(\bar{X}_1, \bar{X}_2\) are sample means, \(s_1^2, s_2^2\) are sample variances, and \(n_1, n_2\) are sample sizes.

A note on P-values: \(p\)-value = \(P(T > t)\), where \(T\) is the test statistic under the null hypothesis. A small p-value (typically \(p\) < 0.05) indicates strong evidence against the null hypothesis, leading to its rejection.

Let’s get an idea of the reasons behind the inclusion of genes in the GenAge database. The dataset contains a column named why, which provides insight into the rationale for each gene’s inclusion.

Let’s perform a significance test comparing genes associated with different senescence mechanisms:

Interpretation

Since our \(p\)-value is \(0.1375\), we fail to reject the null hypothesis, suggesting no significant difference in the GenAge ID distributions between mammal-associated and cell-associated senescence genes.

A heatmap like this can reveal potential biological relationships and pathways that work together in the aging process.

This code analyzes the distribution of genes in the GenAge database by their primary inclusion category, providing insight into how different senescence mechanisms are represented in the dataset.

# Analysis of gene category distribution in senescence database
ggplot(top_15, aes(x = reorder(why, count), y = count)) +
  geom_bar(stat = "identity", fill = "steelblue") +
  coord_flip() +
  labs(
    title = "Distribution of Genes by Senescence Mechanism",
    x = "Senescence Mechanism Category",
    y = "Number of Genes in GenAge Database"
  ) +
  theme_minimal() +
  theme(
    plot.title = element_text(size = 16, face = "bold", hjust = 0.5),
    axis.title = element_text(size = 14),
    axis.text.y = element_text(size = 9),
    axis.text.x = element_text(size = 10),
    panel.grid.major.y = element_blank(),
    panel.grid.minor = element_blank()
  )